Sensitivity Analysis - Dynardo GmbH

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Design Optimization Gradient-Based Algorithms Response Surface Method Evolutionary Algorithm Local Adaptive RSM Pareto Optimization Global Adaptive RSM © 2009 ANSYS, Inc. All rights reserved. 31 ANSYS, Inc. Proprietary
Optimization Strategy <strong>Sensitivity</strong> <strong>Analysis</strong> • Shows Potential • Indicates multiple local optima • No parameter reduction Strategy: • Global search, EA • Start design(s) from SA • Local improvement, ARSM initial © 2009 ANSYS, Inc. All rights reserved. 33 ANSYS, Inc. Proprietary
Page 1 and 2: Optimization and Robustness Evaluat
Page 3 and 4: Motivation Power Plant 1000 MW Effi
Page 5 and 6: Parameterization Process & Geometry
Page 7 and 8: Parameterization of the Geometry De
Page 9 and 10: Parameter Manager Parameter List In
Page 11 and 12: Workbench Interface to optiSLang ©
Page 13 and 14: Parameter Physics, Fluid Flow c0 r
Page 17 and 18: Latin Hypercube and Confidences •
Page 19 and 20: Coefficient of Importance, CoI CoI=
Page 21 and 22: Meta-Model of Prognosis, MoP Respon
Page 23 and 24: Coefficient of Importance, CoI vs.
Page 25 and 26: Anthill Plots Objective initial Goo
Page 27: Parameterization Process & Geometry
Page 31 and 32: Evolutionary Algorithms (EA) Optimi
Page 33 and 34: Evolutionary Algorithms (EA) best d
Page 35 and 36: Adaptive Response Surface (ARSM)
Page 37 and 38: Initial vs. Optimized Design Input
Page 39 and 40: Initial vs. Optimized Design Isotro
Page 43 and 44: Variation of Robust Space • n = 1
Page 45 and 46: Define Objective Limits • Limit s
Page 47 and 48: Evaluation of Histogram Non-robust

Sensitivity Analysis - Dynardo GmbH

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